Question 1

Use the regression output below to answer the following questions.
 Lingar Reperaidion Anthyis: Dep Var $( X ) =$ Weight 
$X = \{ A$ Ee, Gender, Heifht, MBA, $Y$ ear $\}$
$\begin{array}{||c|c|c|c|c|c|}
\hline \text { Coefficients } & \boldsymbol{b} & \begin{array}{c}
\text { Std. } \
\text { Error }
\end{array} & \begin{array}{c}
\text { Std. } \
\text { Beta }
\end{array} & \begin{array}{c}
\boldsymbol{t} \text {-test } \
\text { Statistic }
\end{array} & \begin{array}{c}
\boldsymbol{p} \text {-value } \
\text { Two Tailed }
\end{array} \
\hline \text { Intercept } & -210.603 & 20.560 & & -10.243 & 0.0000 \
\hline \text { Age } & 0.660 & 0.279 & 0.101 & 2.363 & 0.0186 \
\hline \text { Gender } & 17.449 & 2.450 & 0.267 & 7.122 & 0.0000 \
\hline \text { Height } & 4.999 & 0.294 & 0.613 & 16.982 & 0.0000 \
\hline \text { MBA } & -3.122 & 3.063 & -0.043 & -1.019 & 0.3087 \
\hline \text { Year } & -0.111 & 0.507 & -0.006 & -0.218 & 0.8274 \
\hline
\end{array}$
$\begin{array}{|c|c|c|c|c|}
\hline \boldsymbol{r} & \boldsymbol{r}^{2} & \text { Adj. } \boldsymbol{r}^{2} & \mathrm{SE}(\mathrm{Reg}) & \boldsymbol{h} \
\hline 0.834 & 0.696 & 0.693 & 17.879 & 448 \
\hline \hline
\end{array}$
$\begin{array}{|c|c|c|c|c|c|}
\hline \begin{array}{c}
\text { Source of } \
\text { Variation }
\end{array} & \begin{array}{c}
\text { Sum of } \
\text { Squares }
\end{array} & \boldsymbol{d f} & \begin{array}{c}
\text { Mean } \
\text { Squares }
\end{array} & \begin{array}{c}
\text { F-test } \
\text { Statistic }
\end{array} & \begin{array}{c}
\boldsymbol{p} \text {-value } \
\text { One Tailed }
\end{array} \
\hline \text { Regression } & 323592.24 & 5 & 64718.4 & 202.471 & 0.0000 \
\hline \text { Error } & 141282.23 & 442 & 319.643 & & \
\hline \text { Total } & 464874.47 & 447 & & & \
\hline
\end{array}$

-Based on the regression printout,what would be the predicted (mean)weight of a female,20 years old,68 inches tall,without an MBA,and in Year 1.

Accepted Answer

The answer of Use the regression output below to answer...

Question 2

To determine if the regression coefficients (b's)in a regression model are significantly different from zero,the _______________ is used.

Accepted Answer

A)  F-test 
B)  t-test 
C)  chi-square 
D)  significance level

Question 3

Each independent coefficient ($\beta$)in a regression model will show the impact on the dependent variable of a one-unit change in the corresponding independent variable,if all of the other variables are held constant.

Accepted Answer

A) True 
 B)False

Question 4

One way to correct for heteroscedasticity is to transform either the independent variables or the dependent variable using logarithms.

Accepted Answer

A) True 
 B)False

Question 5

Describe how transformations can be used to apply linear regression to nonlinear relationships.

Accepted Answer

The answer of Describe how transformations can be used to...

Question 6

-What does the Constant signify in this regression?

Accepted Answer

A)  the probability of Gender=1 when no other variables are changed 
B)  the average age of males in the sample 
C)  the average age of females in the sample 
D)  It does not signify anything useful here.

Question 7

The model used most often in marketing that includes item-specific information is referred to as the

Accepted Answer

A)  Gumbel Logit model (GLM) 
B)  Multinomial Logit model (MNL) 
C)  Guadagni & Little Logit model (GLLM) 
D)  Kaiser Probit model (KPM)

Question 8

If the dependent variable is count data,the regression model that will be used is the

Accepted Answer

A)  multinomial logit regression model 
B)  discrete choice model 
C)  exploded logit regression model 
D)  Poisson regression model

Question 9

In regression analysis,the F-test tells you if all of the variables taken together help explain the variation in the dependent variable.

Accepted Answer

A) True 
 B)False

Question 10

Use the regression output below to answer the following questions.
 Lingar Reperaidion Anthyis: Dep Var
 $( X ) =$ Weight $X = \{ A$ Ee, Gender, Heifht, MBA, $Y$ ear $\}$
$\begin{array}{|c|c|c|c|c|c|}
\hline \text { Coefficients } & \boldsymbol{b} & \begin{array}{c}
\text { Std. } \
\text { Error }
\end{array} & \begin{array}{c}
\text { Std. } \
\text { Beta }
\end{array} & \begin{array}{c}
\boldsymbol{t} \text {-test } \
\text { Statistic }
\end{array} & \begin{array}{c}
\boldsymbol{p} \text {-value } \
\text { Two Tailed }
\end{array} \
\hline \text { Intercept } & -210.603 & 20.560 & & -10.243 & 0.0000 \
\hline \text { Age } & 0.660 & 0.279 & 0.101 & 2.363 & 0.0186 \
\hline \text { Gender } & 17.449 & 2.450 & 0.267 & 7.122 & 0.0000 \
\hline \text { Height } & 4.999 & 0.294 & 0.613 & 16.982 & 0.0000 \
\hline \text { MBA } & -3.122 & 3.063 & -0.043 & -1.019 & 0.3087 \
\hline \text { Year } & -0.111 & 0.507 & -0.006 & -0.218 & 0.8274 \
\hline
\end{array}$

$\begin{array}{|c|c|c|c|c|}
\hline \boldsymbol{r} & \boldsymbol{r}^{2} & \text { Adj. } \boldsymbol{r}^{2} & \text { SE(Reg) } & \boldsymbol{n} \
\hline 0.834 & 0.696 & 0.693 & 17.879 & 448 \
\hline
\end{array}$

$\begin{array}{|c|c|c|c|c|c|}
\hline \begin{array}{c}
\text { Source of } \
\text { Variation }
\end{array} & \begin{array}{c}
\text { Sum of } \
\text { Squares }
\end{array} & \text { df } & \begin{array}{c}
\text { Mean } \
\text { Squares }
\end{array} & \begin{array}{c}
F \text {-test } \
\text { Statistic }
\end{array} & \begin{array}{c}
\boldsymbol{p} \text {-value } \
\text { One Tailed }
\end{array} \
\hline \text { Regression } & 323592.24 & 5 & 64718.4 & 202.471 & 0.0000 \
\hline \text { Error } & 141282.23 & 442 & 319.643 & & \
\hline \text { Total } & 464874.47 & 447 & & & \
\hline
\end{array}$

-Which variable(s)is/are (a)significant,strong predictor(s)of Weight in the regression above?

Accepted Answer

A)  Age and Height 
B)  Gender only 
C)  Height only 
D)  Age, Height, Gender, MBA and Year 
E)  Height and Gender

Question 11

The value of r2 will always increase when adding additional variables to a multiple linear regression equation.

Accepted Answer

A) True 
 B)False

Question 12

Standardized residuals are useful in seeing whether there are any strong outliers.

Accepted Answer

A) True 
 B)False

Question 13

_______________ takes both binary and interval data and produces probabilities of an outcome,such as purchase probabilities and market share projections,as functions of the marketing mix.

Accepted Answer

A)  The weighted least squares model 
B)  Logistic regression 
C)  The Breusch-Pagan model 
D)  The discrete choice model

Question 14

-Based on the binary regression output above,which of the independent variable(s)are significant predictors of Gender?

Accepted Answer

A)  Age and Weight 
B)  Age and Height 
C)  Height and Weight 
D)  Age, Height and Weight 
E)  all of the variables are significant
F) none of the variables is significant

Question 15

Use the regression output below to answer the following questions.
 Lingar Reperaidion Anthyis: Dep Var $( X ) =$ Weight 
$X = \{ A$ Ee, Gender, Heifht, MBA, $Y$ ear $\}$
$\begin{array}{||c|c|c|c|c|c|}
\hline \text { Coefficients } & \boldsymbol{b} & \begin{array}{c}
\text { Std. } \
\text { Error }
\end{array} & \begin{array}{c}
\text { Std. } \
\text { Beta }
\end{array} & \begin{array}{c}
\boldsymbol{t} \text {-test } \
\text { Statistic }
\end{array} & \begin{array}{c}
\boldsymbol{p} \text {-value } \
\text { Two Tailed }
\end{array} \
\hline \text { Intercept } & -210.603 & 20.560 & & -10.243 & 0.0000 \
\hline \text { Age } & 0.660 & 0.279 & 0.101 & 2.363 & 0.0186 \
\hline \text { Gender } & 17.449 & 2.450 & 0.267 & 7.122 & 0.0000 \
\hline \text { Height } & 4.999 & 0.294 & 0.613 & 16.982 & 0.0000 \
\hline \text { MBA } & -3.122 & 3.063 & -0.043 & -1.019 & 0.3087 \
\hline \text { Year } & -0.111 & 0.507 & -0.006 & -0.218 & 0.8274 \
\hline
\end{array}$
$\begin{array}{|c|c|c|c|c|}
\hline \boldsymbol{r} & \boldsymbol{r}^{2} & \text { Adj. } \boldsymbol{r}^{2} & \mathrm{SE}(\mathrm{Reg}) & \boldsymbol{h} \
\hline 0.834 & 0.696 & 0.693 & 17.879 & 448 \
\hline \hline
\end{array}$
$\begin{array}{|c|c|c|c|c|c|}
\hline \begin{array}{c}
\text { Source of } \
\text { Variation }
\end{array} & \begin{array}{c}
\text { Sum of } \
\text { Squares }
\end{array} & \boldsymbol{d f} & \begin{array}{c}
\text { Mean } \
\text { Squares }
\end{array} & \begin{array}{c}
\text { F-test } \
\text { Statistic }
\end{array} & \begin{array}{c}
\boldsymbol{p} \text {-value } \
\text { One Tailed }
\end{array} \
\hline \text { Regression } & 323592.24 & 5 & 64718.4 & 202.471 & 0.0000 \
\hline \text { Error } & 141282.23 & 442 & 319.643 & & \
\hline \text { Total } & 464874.47 & 447 & & & \
\hline
\end{array}$

-Write the regression equation for the regression output above.

Accepted Answer

The answer of Use the regression output below to answer...

Question 16

Autocorrelation occurs when the error does not have a constant variance.

Accepted Answer

A) True 
 B)False

Question 17

If the dependent variable is a nominal scale,then the proper regression model to use is

Accepted Answer

A)  multinomial regression 
B)  probit model 
C)  ordered logit model 
D)  linear regression after a chi square transformation 
E)  count regression

Question 18

Nonlinear relationships can be examine with ordinary linear regression through transformation functions such as logs,exponents,squares,square roots,and polynomials.

Accepted Answer

A) True 
 B)False

Question 19

For linear regression,the assumption is made that the error term is

Accepted Answer

A)  normally distributed with a mean of 1 
B)  normally distributed with a mean of 0 
C)  the sum of the standard deviations of all of the $\beta$ values 
D)  the sum-of-squares explained by the regression model

Question 20

To determine if the fit of a regression equation is larger than the error,the _______________ is used.

Accepted Answer

A)  F-test 
B)  t-test 
C)  chi-square 
D)  significance level

Exam 9: Multiple Regression: Modeling Multivariate Relationships

To determine if the regression coefficients (b's) in a regression model are significantly different from zero,the _______________ is used.

Each independent coefficient ( $\beta$ )in a regression model will show the impact on the dependent variable of a one-unit change in the corresponding independent variable,if all of the other variables are held constant.

One way to correct for heteroscedasticity is to transform either the independent variables or the dependent variable using logarithms.

Describe how transformations can be used to apply linear regression to nonlinear relationships.

-What does the Constant signify in this regression?

The model used most often in marketing that includes item-specific information is referred to as the

If the dependent variable is count data,the regression model that will be used is the

In regression analysis,the F-test tells you if all of the variables taken together help explain the variation in the dependent variable.

The value of r²will always increase when adding additional variables to a multiple linear regression equation.

Standardized residuals are useful in seeing whether there are any strong outliers.

_______________ takes both binary and interval data and produces probabilities of an outcome,such as purchase probabilities and market share projections,as functions of the marketing mix.

-Based on the binary regression output above,which of the independent variable(s) are significant predictors of Gender?

Autocorrelation occurs when the error does not have a constant variance.

If the dependent variable is a nominal scale,then the proper regression model to use is

Nonlinear relationships can be examine with ordinary linear regression through transformation functions such as logs,exponents,squares,square roots,and polynomials.

For linear regression,the assumption is made that the error term is

To determine if the fit of a regression equation is larger than the error,the _______________ is used.

Exam 9: Multiple Regression: Modeling Multivariate Relationships

To determine if the regression coefficients (b's) in a regression model are significantly different from zero,the _______________ is used.

Each independent coefficient ( β\betaβ )in a regression model will show the impact on the dependent variable of a one-unit change in the corresponding independent variable,if all of the other variables are held constant.

One way to correct for heteroscedasticity is to transform either the independent variables or the dependent variable using logarithms.

Describe how transformations can be used to apply linear regression to nonlinear relationships.

-What does the Constant signify in this regression?

The model used most often in marketing that includes item-specific information is referred to as the

If the dependent variable is count data,the regression model that will be used is the

In regression analysis,the F-test tells you if all of the variables taken together help explain the variation in the dependent variable.

The value of r2 will always increase when adding additional variables to a multiple linear regression equation.

Standardized residuals are useful in seeing whether there are any strong outliers.

_______________ takes both binary and interval data and produces probabilities of an outcome,such as purchase probabilities and market share projections,as functions of the marketing mix.

-Based on the binary regression output above,which of the independent variable(s) are significant predictors of Gender?

Autocorrelation occurs when the error does not have a constant variance.

If the dependent variable is a nominal scale,then the proper regression model to use is

Nonlinear relationships can be examine with ordinary linear regression through transformation functions such as logs,exponents,squares,square roots,and polynomials.

For linear regression,the assumption is made that the error term is

To determine if the fit of a regression equation is larger than the error,the _______________ is used.

Each independent coefficient ( $\beta$ )in a regression model will show the impact on the dependent variable of a one-unit change in the corresponding independent variable,if all of the other variables are held constant.

The value of r²will always increase when adding additional variables to a multiple linear regression equation.